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Dingo

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Dingo: A Comprehensive Data Quality Evaluation Tool

FreeFree tier
Inputs: text
Type
Open Source
Company
MigoXLab
Visit Website GitHub (38 stars)

About Dingo

Dingo is an open-source, comprehensive AI data, model, and application quality evaluation tool designed for ML practitioners, data engineers, and AI researchers. It systematically assesses and improves the quality of training data, fine-tuning datasets, and production AI systems. Key capabilities include production-grade quality checks, multi-source data integration (local files, SQL databases, HuggingFace datasets, S3), multi-field evaluation with parallel rule execution, RAG system assessment using five academic-backed metrics, a hybrid approach combining 30+ built-in heuristic rules with LLM-based deep assessment, flexible execution from local to Apache Spark for billion-scale datasets, and detailed quality reports with GUI visualization and field-level insights. The tool is free and open-source, with an optional SaaS enterprise edition that adds a web UI, access control, visual reports, and a RESTful API.

Key Features

Production-grade quality checks for pre-training, fine-tuning, and production AI systems
Multi-source data integration: local files, SQL databases (PostgreSQL/MySQL/SQLite), HuggingFace datasets, and S3 storage
Multi-field evaluation applying different quality rules to different fields in parallel
RAG system assessment with five academic-backed metrics for retrieval and generation quality
LLM Rule Agent Hybrid combining 30+ fast heuristic rules with LLM-based deep assessment
Flexible execution: run locally for rapid iteration or scale with Apache Spark for billion-scale datasets
Rich reporting with detailed quality reports, GUI visualization, and field-level insights
Open-source with active community on Discord and WeChat

Pros & Cons

Pros
  • Comprehensive evaluation covering data, model, and application quality
  • Supports multiple data sources including local files, SQL databases, HuggingFace, and S3
  • Hybrid approach combines fast heuristic rules (30+) with LLM-based deep assessment
  • Scalable from local execution to Apache Spark for billion-scale datasets
  • Detailed quality reports with GUI visualization and field-level insights
  • Open-source and free with active community support (Discord, WeChat)
  • RAG evaluation uses five academic-backed metrics for rigorous assessment
Cons
  • Requires Python programming knowledge for usage and integration
  • No built-in GUI in the open-source version (web UI available only in SaaS enterprise edition)
  • Some features require additional dependencies (e.g., HHEM hallucination detection requires transformers + torch, retrieval evaluation requires MTEB and pytrec-eval-terrier)
  • May have a learning curve for configuring multi-field evaluation and custom rules

Best For

Pre-training data quality evaluationFine-tuning dataset assessmentRAG system retrieval and generation quality evaluationLLM chat data evaluationProduction AI system quality monitoringData governance and data quality assurance for enterprise AI pipelines

FAQ

Is Dingo free?
Yes, Dingo is open-source and free to use. There is also a SaaS enterprise edition with additional features like a web UI, access control, visual reports, and a RESTful API.
What data sources does Dingo support?
Dingo supports local files, SQL databases (PostgreSQL, MySQL, SQLite), HuggingFace datasets, and S3 storage.
How does Dingo evaluate RAG systems?
Dingo uses five academic-backed metrics to comprehensively assess retrieval and generation quality in RAG systems.
Can Dingo handle large datasets?
Yes, Dingo can run locally for rapid iteration or scale with Apache Spark for billion-scale datasets.
Does Dingo have a graphical user interface?
The open-source version is a Python library without a GUI. The SaaS enterprise edition provides a web-based visual interface.